Senior Data Engineer - Ontology & Knowledge Graphs
AI & Data · London · Full time · Senior level
This role is about turning messy information from many places into clean, connected data that applications and AI tools can rely on. A strong candidate writes solid Python and SQL, has run a graph database or triple store in production, and has designed ontologies with clear definitions and versioning. Handling duplicate records, conflicting sources, and permissions that must carry through to derived data matters as much as the modelling itself.
How it fits the AI era
AI tools can speed up routine pipeline code, draft schema ideas, and help match records, but a knowledge graph is only useful if its facts can be trusted. Judgement about what an entity really is, how to handle uncertain or conflicting sources, and how to keep access rules intact stays with people. Here the graph also feeds AI assistants, so traceable answers and tested data quality directly shape how reliable those tools are.
How it builds AI experience
The work sits underneath an AI platform used inside secure, controlled environments, so the experience gained is in giving AI systems structured, permission-aware context and traceable results. Someone could prepare by building a small knowledge graph in Neo4j or an RDF store, then connecting it to a retrieval tool that cites where each answer came from. Practice with entity matching and provenance tracking would be especially relevant.
Worth showing
- Production graph database work
- Ontology design with versioning
- Entity resolution and provenance
- Permission-preserving data models
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Valarian Technologies builds sovereign digital infrastructure that enables organizations to operate artificial intelligence and sensitive workloads inside environments under their direct control, acco
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